Projected longer dry spells under climate change occur during dry seasons not wet seasons

By Caroline Wainwright 

The latest Intergovernmental Panel on Climate Change (IPCC) report states that the global water cycle will intensify with continued global warming. This means fewer rainy days, but with more intense rain over many land regions, and more variability generally1 . More dry days and longer dry spells have the potential to lead to negative impacts on crop yields and food security, as reductions in water availability limit crop growth. The impacts on crops also depend on the timing of these longer dry spells in the annual cycle and future delays in the wet season are also reported in the IPCC report and by my previous research.

Exploiting the latest state of the art computer simulations, we have examined changes in wet and dry spell characteristics under future climate change across the tropics, applying novel techniques to diagnose wet and dry season changes. The results of our new research are summarised in the schematic below.

We find the start of wet season is projected to be delayed by up to 2 weeks by the end of the 21st century across South America, southern Africa, West Africa, and the Sahel. This is important since it can affect the planting of crops.

We also find a reduction in dry season rainfall and an increase in dry spell length during the dry season across Central and South America, southern Africa, and Australia. Mean dry season dry spell lengths are projected to increase by 5–10 days over northeast South America and southwest Africa. This may make the dry seasons more intense, negatively impacting perennial crops, such as cocoa, and crops grown during the dry season.

However, changes in dry spell length during the wet season are much smaller across the tropics. Therefore, agriculture grown solely during the wet season may be less affected by longer dry spells.

Temperature increases are projected to be larger in dry seasons than in wet seasons, with increases in dry season maximum temperatures found to be up to 3°C higher than the increases in wet season maximum temperatures over South America, southern Africa, and parts of Asia. In these regions, mean maximum temperatures greater than 35oC become more expansive with warming.

Overall, while we find that changes in dry spell length during the wet season are generally small, longer dry spells, fewer wet days, and higher temperatures during the dry season may lead to increasing dry season aridity and have detrimental consequences for perennial crops. As the latest IPCC report states, limiting human-induced global warming and associated changes in the water cycle requires rapid and sustained cuts in CO2, such that emissions are balanced by additional uptake by the land and ocean, along with strong reductions in other greenhouse gas emissions.Figure 1: Schematic summarizing the changes in wet/dry season rainfall and wet/dry spell lengths in wet/dry seasons found here; the top panel is for dry seasons and the bottom row is for wet seasons (including regions that are wet year-round). (top) Longer dry spells and lower rainfall during the dry season are found over Central and South America and southern Africa. Shorter dry spells and more rainfall during the dry season are found over East Africa and parts of Asia and the Sahel. (bottom) More rainfall in the wet season is found over East Africa and Asia. Less rainfall in the wet season is found over northern South America. Reductions in the length of wet spells in the wet season are found over South America and West and Central Africa.

References

Douville, H. et al. (2021) Water Cycle Changes. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, In press [Masson-Delmotte, V., et al.] www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter_08.pdf

Dunning C.M., Black, E. and Allan, R.P. (2018), Later wet seasons with more intense rainfall over Africa under future climate change, J. Climate, 31, 9719-9738, doi: 10.1175/JCLI-D-18-0102.1.

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K.
Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press. https://www.ipcc.ch/report/ar6/wg1 

Wainwright, C. M., Black, E., and Allan, R. P. (2021). Future Changes in Wet and Dry Season Characteristics in CMIP5 and CMIP6 Simulations. Journal of Hydrometeorology 22, 9, 2339-2357, doi: 10.1175/JHM-D-21-0017.1

 

Posted in Climate, Climate change, Monsoons, Rainfall, Water cycle | Leave a comment

From Falling Paper Strips, Tossed Coins To Settling Snowflakes

By Majid Hassan Khan

Did you notice money raining down in part three of the Spanish TV series “Money Heist” (Spanish: La casa de Papel, “The House of Paper”) on Netflix? A blimp flew over Madrid and showered money. These falling paper bills fluttered, tumbled and followed random trajectories while descending to the streets. The behaviour has a similarity with the falling of leaves from a tree, descent of snowflakes and path of tossed coins in fountains and wishing wells.

It is a wonder why Newton never addressed the behaviour of falling leaves from the apple tree in his garden at Woolsthorpe Manor. He did perform experiments by dropping glass spheres and inflated hog bladders from a cathedral in London. A look at the influence of air around falling leaves or freely falling paper strips or the behaviour of water around a flipped coin in a pool was to come later. It took another two hundred years before a 22-year-old James Clerk Maxwell paid heed to the physics around falling paper slips. He observed the flutter and explained the changes in pressure distribution around the strip and even referred to the resistance offered to the paper by the air around it. Similar is the nature of fall when coins are tossed in fountains, pools and rivers. Falling coins rotate, flutter, tumble and descend on different trajectories.

A common term used to emphasize the similarity in physics between air and water is ‘fluid’. The story of fluid dynamics can be woven using ‘inertia’ and ‘friction’ of the flowing medium as the central characters. The idea of friction in fluids was proposed by many scientists who contributed to the evolution of the physics of fluids. Newton being the first, followed by Poiseuille and Hagen. Later Stokes further emphasised the notion of friction in fluids. The physics of fluids was evolving, and newer ideas were included to aid a better understanding. When Reynolds demonstrated the onset of turbulence in flow and Prandtl explained the effect of viscosity close to the surfaces of an object immersed in the flow, newer insights in the understanding of external flows started taking shape. Now it is widely known that the storyline is influenced by the geometry of the object around which the flow happens. Inertia and viscosity swap the roles of being the main protagonist based on the size and shape of the object around which the flow is observed.

With recent advances in fluid dynamics, we can explain that the flow physics of a freely falling paper strip is influenced by its initial state, the size and symmetry of the falling paper strip, the density difference of falling paper and air, and the viscosity of the fluid. The flows separating at the edges of the strip roll in vortices leaving an unsteady trail behind the falling paper. This unsteadiness causes an imbalance in the forces acting on the paper strip. The extent of the force imbalance on the paper strip due to the flowing fluid around it causes it to fall steadily, flutter, rotate, tumble, drift, oscillate or stably fall. All these fall behaviours are direct outcomes of the interaction of the air and the paper strip. Similar interactions happen when a coin is tossed in a wishing well or a pool of water. Flow separates at the edge of the coin, vortices are formed and shed in the wake. The coin falls steadily, flutters, rotates or shows chaotic behaviour. The physics of these simple freely falling objects when extended to ice particles and snowflakes helps understand the fall behaviour of snowflakes and the nature of flow around them which aids in explaining their growth rates.

To appreciate the fall behaviour of a freely falling paper Maxwell used a paper strip with sides two inches long and one inch wide. You can try dropping paper strips of varied sizes and observing their fall. I suggest you cut a paper strip 1 cm wide and about 5 to 10 cm long and drop it from a height with the larger side slightly tilted away from the horizontal (and the flat face of the strip looking at you). You will be amazed by the fall behaviour. After the initial settling the paper will orient itself in one direction of fall and will start spinning around an axis parallel to the longest side (an example of autorotation).  If you get the feel of the fall, you can use different shapes and enjoy the diverse fall behaviour of simple, harmless paper pieces and marvel at the complexity of the fluid dynamics of freely falling paper strip.

The understanding of fluid object interaction is of immense use in environmental science, atmospheric physics, insect flights and industrial needs. In meteorology, clarity about the fluid dynamics of falling objects helps establish the fate of ice particles and snowflakes in the atmosphere and in clouds.

Posted in Climate, Fluid-dynamics | Leave a comment

Data assimilation under dramatic growth of observational data and rapid advances in computer performance

By: Guannan Hu

The importance of data assimilation

Data assimilation (DA) is a technique used to produce initial conditions for numerical weather prediction (NWP). In NWP, computer models describing the evolution of the atmosphere are used to predict future weather based on current or previous weather conditions. These models are usually very sensitive to initial conditions, meaning that slight changes in the initial conditions can lead to completely different weather forecasts. The Data Assimilation for the Resilient City (DARE) project is investigating the use of novel observations such as temperature data from vehicles, smartphone data, river camera images, etc. for weather and flooding forecasting. Accurate forecasting of hazardous weather events can help us prepare in advance to protect lives and property and reduce economic losses.

DA is also used to create climate reanalyses, which are gridded datasets providing long-term historical estimates of climate variables covering the globe or a region. These datasets are used to monitor climate change.

The basic idea of data assimilation

Data assimilation blends observations with model forecasts to produce the best estimates of atmospheric and climate variables. For example, the air temperature on campus can be measured by a thermometer or predicted from past temperatures (and other relevant variables such as humidity and wind) using a computer model. Then we obtain the estimates for air temperature from two sources. We assume that the true temperature is somewhere in between. It can therefore be given by a weighted average of the two estimates, where the one with the smaller error has the greater weight as it is considered more reliable. This is a very simple example; the real data assimilation applications are much more complex and involve a huge amount of data.

The assimilation of novel observations

As computers become more powerful and the volume of observational data increases rapidly, data assimilation becomes increasingly important in improving the skills of weather forecasting. The assimilation of novel observations (e.g., geostationary satellite, radar data) has led to great improvement in forecast skill. Unlike thermometers and other conventional instruments, the weather satellite and Doppler radar measure the atmospheric variables indirectly. These observations need to be transformed for use in data assimilation procedures. This will cause so-called representation errors in addition to measurement errors. The observation error (includes representation and measurement errors) been found to be spatially correlated for some observation types, such as geostationary satellite data and Doppler radar radial wind. In practical applications, these error correlations are usually taken into account indirectly in data assimilation systems or removed by thinning the observations. These approaches might be suboptimal as they prevent us from making full use of the observations. Accurately estimating observation error correlations for satellite and radar data can be very challenging. Satellite observations can have a mixture of inter-channel and spatial error correlations. Doppler radar radial wind has the error correlation lengthscales that may not be isotropic; they vary with the height of the observations and the distance of the observations to the radar. In addition, explicitly including correlated observation error statistics may largely increase the computational cost of DA. The increase is mainly caused by the inversion of dense matrices and the parallel communication costs in the computation of matrix-vector products. Another issue with including correlated error statistics is that it may change the convergence behaviour of the minimization procedure in variational data assimilation, which solves a least-square problem.

The more and more wide application of data assimilation

Starting with its use in the NWP, DA is now attracting more and more interest from the wider scientific community. People with different backgrounds and from different research institutes, universities, and weather services around the world are not only committed to developing new methods but are also keen to apply this technique to new areas. For instance, DA can be combined with machine learning. DA can be applied to space weather forecasting and even used to monitor and predict a pandemic!

 

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How do we actually run very high resolution climate simulations?

By: Annette Osprey

High resolution modelling

Running very detailed and fine scale (“high resolution”) simulations of the Earth’s atmosphere is vital for understanding changes to the Earth’s climate, particularly extreme events and high-impact weather [1]. However, each simulation is 1) time-consuming to set up – scientists spend a lot of time designing the experiments and perfecting the underlying science, and 2) expensive to run – it may take many months to complete a multi-decade simulation on thousands of CPUs. But the data from each simulation may be used many times for many different purposes.

Under the hood

There is a lot of technical work that is done “under the hood” to make sure the simulations run as seamlessly and efficiently as possible and the results safely moved to a data archive where they can be made available to others. This is the work that we do in NCAS-CMS (the National Centre for Atmospheric Science’s Computational Modelling Services group), alongside our colleagues at CEDA (the Centre for Environmental Data Analysis) and the UK Met Office. My role is to work with the HRCM (High Resolution Climate Modelling) team, helping scientists to set up and manage these very large-scale simulations.

CMS is responsible for making sure the simulation code, the Met Office Unified Model (UM), runs on the national supercomputer, Archer2, for academic researchers around the UK. As well as building, testing and debugging different versions of the code, we need to install the supporting software that is required to actually run the UM (we call this the “software infrastructure”). This includes code libraries, experiment and workflow management tools [2], and software for processing input and output data. This is all specialist code that we need to configure for our particular systems and the needs of our users, and sometimes we need to supplement this with our own code.

Robust workflows

We call the end-to-end process of running a simulation the “workflow”. This involves 1) setting up the experiment (selecting the code version, scientific settings, and input data), 2) running the simulation on the supercomputer, 3) processing the output data, 4) then archiving the data to the national data centre Jasmin, where we can look at the results and share with other scientists. When running very high resolution and/or long-running simulations we need this process to be as seamless as possible. We don’t want to have to keep manually restarting the experiment or troubleshooting technical issues.

Furthermore, the volume of data that is generated from these high resolution simulations is incredibly large. It is too large to store all the data on the supercomputer, and it can sometimes take as long as the simulation to move the data to the archive. The solution therefore, is to process and archive the data as the simulation is running. We build this into the workflow so that it can be done automatically, and we have as many of the tasks running at the same time as possible (this is known as “concurrency”).

The HRCM workflow

 

 

 

 

 

 

 

Figure 1: An example workflow for a UM simulation with data archiving to Jasmin, showing several tasks running concurrently.

The image shows the workflow we have set up for our latest high resolution simulations. We split the simulation into chunks, running 1 month at a time. Once one month has completed, we set the next month running and begin processing the data we just produced. The workflow design means that the processing can be done at the same time as the next simulation month is running. First we perform any transformations on the data, then we begin copying it to Jasmin. We generate unique hashes (checksums) that we use to verify the data copy is identical to the original, so that we can safely delete it, clearing space for forthcoming data. Then we upload the data to the Jasmin long term tape archive, and we may put some files in a workspace where scientists can review the progress of the simulation.

Helping climate scientists get on with science

The advances that we make for the high resolution simulations are made available to our other users, whatever the size of the run. Ideally the workflow design means that the only user involvement is to start the run going. In reality, of course, sometimes the machine goes down, connections are lost, the model crashes, (or the experiment wasn’t set up correctly!) Thus, we have built a level of resilience into our workflow that means that we can deal with failures effectively. So, scientists can focus on setting up the experiment and analysing the results, without worrying too much about how the simulation runs.

References

[1] Roberts, M. J., et al. (2018). “The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale” in Bulletin of the American Meteorological Society, 99(11), 2341-2359, doi: https://doi.org/10.1175/BAMS-D-15-00320.1

[2] H. Oliver et al. (2019). “Workflow Automation for Cycling Systems,” in Computing in Science & Engineering, 21(4), 7-21, doi: https://doi.org/10.1109/MCSE.2019.2906593.

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The Other Climate Impact Of Aviation

By: Ella Gilbert

In-flight entertainment

Picture yourself in the window seat of an aeroplane, cruising along at 30,000 feet, occasionally admiring the clouds below and watching that cheesy blockbuster you were too shy to go and see in the cinema. If you’re like me, you might also be trying not to think about the impact of this flight on the climate – after all, we are increasingly reminded that travelling by air is one of the most carbon-intensive things we can do. 

But when you hear the phrase ‘climate impacts of aviation’, chances are you think about the emissions of greenhouse gases like carbon dioxide (CO2) from aircraft. Unfortunately, that’s only a third of the story. What you probably don’t think about are the non-CO2 impacts, which have a climate warming effect twice as large. Bad news if you’re already worried about that flight.

Flying from London to Inverness, for example, is equivalent to eating 13 beef steaks if we consider the CO2 emissions alone, while if we consider the non-CO2 effects it’s more like 24. And if you’re flying from London to San Francisco, those numbers rise to a whopper-ing 117 and 224 steaks[1]. Now, how’s that for an in-flight meal?

It’s not just CO2

Many of the non-CO2 impacts of aviation act in opposing directions. Some cool the atmosphere overall, while others warm it. To make matters more complicated, some effects even have different impacts on the climate over different timescales. Because these non-CO2 impacts are so complex and difficult to observe, there is still a great deal of uncertainty around their magnitude.

Advancing the Science for Aviation and Climate (ACACIA) is a multi-institutional European project trying to dispel some of the ambiguity about the various effects of aviation on climate, many of which you can see on the schematic below. At the University of Reading, we’re working on one of the most uncertain impacts: the effect of aviation aerosol-cloud interactions.

Figure 1: – Schematic overview of how aviation impacts the climate. From Lee et al. (2021)

 Aircraft emit lots of gases and particles at the high altitudes where they fly. Their exhaust plumes spew gases like CO2, nitrogen oxide (NOx) and water vapour, as well as soot and sulphur particles into the atmosphere.

Those soot and sulphur particles are also known as aerosols, and they act like tiny seeds on which ice crystals and liquid droplets can grow. In the right conditions, soot aerosols can trigger the formation of ice crystals, which make up cirrus clouds – the wispy, indistinct clouds you see high up in the sky.

A cloudy blanket

Cirrus clouds tend to warm the Earth overall. That’s because they are very thin and so let solar energy travel through them easily, but at the same time absorb lots of outgoing infrared radiation, preventing it from escaping to space and so warming the surface like a blanket (aka the Greenhouse effect). But aerosols change the properties of those cirrus clouds in ways we’re still learning about.

Think about your flight blazing its way through the sky, its engines releasing aerosols into the atmosphere. As long as the conditions are right for cloud formation, the more aerosols there are in the exhaust plume to act as seeds, the more ice crystals that will form in its wake.

Cloud properties like the number, size and mass of ice crystals influence a cloud’s ‘optical thickness’, which describes how easily radiation can travel through it and so the degree to which those clouds warm or cool the atmosphere.

It’s cirrus-ly complicated

Different characteristics of the cloud compete with each other to push the balance in favour of warming or cooling. For instance, clouds containing many small ice crystals will stick around for longer because it takes more time for crystals to get big enough to fall out of the cloud. Very small crystals (a few thousandths of a centimetre across) tend to reflect more solar energy back to space, which has a cooling effect, but most cirrus clouds contain ice crystals that are larger than this, and so have an overall warming effect.

Aircraft can change how much cirrus clouds warm the climate by injecting more aerosols into atmosphere and influencing how many ice crystals form, as well as their size, shape and lifetime.

Aviation-aerosol-cloud interactions are hugely complex and difficult to measure. And because cloud processes push and pull in different directions, we’re still finding out how aircraft aerosol emissions influence the overall characteristics of cirrus clouds. In fact, the question marks are so large that we don’t actually have a precise number to tell us whether their impact is to warm or cool the atmosphere.

Evidence suggests that it’s probably a warming effect, although a recent review study was unable to provide a best estimate of the effect of aerosol-cloud interactions, leaving a conspicuous gap, and an even newer study shows that the warming impact of aviation-aerosol-interactions may be negligible.

One thing at least is clear: it’s still very much a hot topic of research.

Filling in the blanks

Enter, stage left: ACACIA. Our main task at Reading as part of the ACACIA project is to use very fine-scale computer models (called large eddy simulation, or LES) to explore the processes acting on pre-existing cirrus clouds and to find out how they interact with emissions of aviation aerosols like soot.

Understanding these processes will help us quantify the exact effect of aviation aerosols on cirrus clouds: for instance, how do they impact the number of ice crystals that form? How fast do these crystals grow? How quickly do they disappear? How do the prevailing weather conditions impact these effects?

Reducing the non-CO2 impacts of aviation

Hopefully, the work of the ACACIA project will allow us to fill in some of the blanks when it comes to aviation’s effect on climate – the crucial first-step that will allow us to mitigate its effects. Understanding the science is key, and will allow us to develop solutions that reduce the non-CO2 impacts of aviation.

Using aviation fuels that have less soot, avoiding areas where contrails and cirrus clouds preferentially form or avoiding some airspaces entirely might all be helpful solutions – but more work is needed before these strategies can be implemented, especially because there is no clear winner and many proposed options come with trade-offs like increased CO2 emissions.

So – for now at least – your flight won’t be getting diverted away from those spectacular cirrus clouds. I’ll let you get back to watching Fast and Furious 82 now.

 References:

Defra/BEIS Greenhouse Gas Conversion Factors 2019

Kärcher, B. (2018). Formation and radiative forcing of contrail cirrus. Nature Communications 9, 1824 https://doi.org/10.1038/s41467-018-04068-0

Kärcher, B., Mahrt, F. and Marcolli, C. (2021). Process-oriented analysis of aircraft soot-cirrus interaction constrains the climate impact of aviation. Nature Communications Earth & Environment 2, 113. https://doi.org/10.1038/s43247-021-00175-x 

Lee, D. S. and Coauthors (2021). The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmospheric Environment, 244, 117834. https://doi.org/10.1016/j.atmosenv.2020.117834

Lee, D. S. (2021) Contrails from aeroplanes warm the planet – here’s how new low-soot fuels can help. The Conversation 18 June 2021. Accessed 26/07/2021. Available at: https://theconversation.com/contrails-from-aeroplanes-warm-the-planet-heres-how-new-low-soot-fuels-can-help-162779  

Liou, K.-N. (2005). Cirrus clouds and climate. AccessScience. Retrieved July 26, 2021, from https://doi.org/10.1036/1097-8542.YB050210

Lynch, D.K. (1996) Cirrus clouds: Their role in climate and global change. Acta Astronautica 38 (11), 859-863. https://doi.org/10.1016/S0094-5765(96)00098-7

Niklaß, M., Lührs, B., Grewe, V., Dahlmann, K., Luchkova, T., Linke, F. and Gollnick, V. (2019) Potential to reduce the climate impact of aviation by climate restricted airspaces. Transport Policy 83 102-110. https://doi.org/10.1016/j.tranpol.2016.12.010

Poore, J. and Nemecek, T. (2018) Reducing food’s environmental impacts through producers and consumers. Science 360 (6392) 987-992. https://doi.org/10.1126/science.aaq0216

Shine, K. and Lee, D. S. (2021) Commentary: Navigational avoidance of contrails to mitigate aviation’s climate impact may seem a good idea – but not yet. Green Air News 22 July 2021. Accessed 23/07/2021. Available at: https://www.greenairnews.com/?p=1421

Skowron, A., Lee, D.S., De León, R.R., Ling, L. L. and Owen, B. (2021) Greater fuel efficiency is potentially preferable to reducing NOx emissions for aviation’s climate impacts. Nature Communications 12, 564. https://doi.org/10.1038/s41467-020-20771-3

Timperley, J. (2017) Explainer: The challenge of tackling aviation’s non-CO2 emissions. Carbon Brief 15 March 2017. Accessed 23/07/2021. Available at: https://www.carbonbrief.org/explainer-challenge-tackling-aviations-non-co2-emissions

Timperley, J. (2020) Should we give up flying for the sake of the climate? BBC Future, Smart guide to climate change. Accessed 23/07/2021. Available at: https://www.bbc.com/future/article/20200218-climate-change-how-to-cut-your-carbon-emissions-when-flying

[1] Assuming an ‘average’ emissions intensity for beef per serving of 7.5 kgCO2e after Poore & Nemecek (2018), average flight distances of 723 km and 8629 km for flights to Inverness and San Francisco, respectively, domestic aviation emissions intensity of 133 g and 121 g per passenger kilometre for CO2 and non-CO2 impacts, respectively, and long-haul aviation emissions intensity of 102 g and 93 g per passenger km for CO2 and non-CO2 effects, respectively, after BEIS/Defra emissions conversion factors 2019. See also: https://www.bbc.co.uk/news/science-environment-46459714

https://www.bbc.co.uk/news/science-environment-49349566

 

 

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Soil Moisture Monitoring with Satellite Radar

By: Keith Morrison-Department of Meteorology & Will Maslanka-Department of Geography & Environmental Science

Everyone knows about the impacts from intense and/or prolonged rainfall – flooding, like that experienced in the Thames Basin during the Summer of 2007, and the Winter of 2013/14. Whilst hard-engineering defences (such as raising the height of riverbanks, or construction of flood defences) can be good at dealing with flooding events by keeping water within the river, they can have negative impacts upon natural processes, such as increased deposition and erosion of sediment, and changes to the wildlife habitat. Some hard-engineering practices, such as straightening river meanders, cause river flows to speed up, potentially leading to greater flood risks downstream. Rather than exacerbating flood risk downstream, soft-engineering practices, such as Natural Flood Management (NFM) can be used to slow the flow of water before it enters the watercourse and store the water upstream.

The NERC-funded LANDWISE project (LAND management in lowland catchments for risk reduction) seeks to assess the impact and effectiveness of realistic and scalable land-based NFM measures, to reduce the risk from surface run-off, and groundwater within the Thames Basin. These land-based measures include the planting of more trees in riparian zones (the area along the riverbank), floodplain restoration, and soil and land management changes. The LANDWISE research is done in a multi-disciplinary fashion, by joining together the collective expertise of hydrologists, geologists, farmers, local flood forums, conservation Non-governmental organisations (NGOs) and policy makers, to maximise the impact of the research, and to ensure that the resulting research is greater than the sum of the individual efforts.

One area of focus is that of soil and land management changes; the impact that differing farming practices (such as crop choice and tillage practices) can have on altering infiltration or storage of rainfall in the soil as soil moisture. Soil moisture retrieval from satellite-based radar observations is well established, with various in-service satellite products. However, the resolution of the products are coarse (>1 km), as they are based on spatially averaged measurements from. Instead, this study utilises the higher resolution available from the Sentinel-1 synthetic aperture radar satellite constellation, to work within farmers’ fields, at scales between 1 km and 100 m.

The radar reflectivity of a soil arises from the dielectric contrast at the air/soil boundary, which is set by the soil type and its moisture state. However, moisture retrieval is complicated by the additional sensitivity of the radar to the surface roughness of the soil. To get around this issue, rather than dealing with absolute soil moisture, the LANDWISE project has been looking at relative surface soil moisture (rSSM) using the TU Wien Change Detection Algorithm [1]. This assumes that both the soil type and surface roughness are static parameters. Thus, short-timescale fluctuations present in the backscatter are indicative only of soil moisture changes. By looking at the relative soil moisture, it is possible to create a moisture time series. In this scheme, observations are scaled between the wettest and driest periods, and assuming that the wettest and driest periods are associated with the largest and smallest backscatter values, respectively.

The LANDWISE project has used data from Sentinel-1 to produce an rSSM time series over the Thames basin between October 2015 to December 2020. Some resolution is sacrificed in order to reduce randomly occurring fluctuations, by spatially averaging the imagery onto a 100m grid. Figure 1a shows a snapshot of the rSSM differences across the Thames Basin on 11th of September 2018. A clear band of higher rSSM values can be seen, with lower values to the north and south of it. This band of higher rSSM values can be attributed to a localised shower (Figure 1b) that passed over prior to the time of the satellite acquisition (approx. 18:00 UTC).

Figure 1a: rSSM values across the Thames Basin for the 11th September 2018. Areas denoted in grey are neglected as they are associated with urban areas.

Figure 1b: 12-hourly rainfall accumulation, before the orbit overpass. Rainfall amounts below 0.25mm in 12 hours have not been plotted for clarity.

Rather than looking at a snapshot, Figure 2 looks at the catchment for the river Kennet, a sub-catchment of the Thames Basin, in terms of the temporal changes in rSSM, in both the spatial (top) and in a 7-orbit smoothing (bottom). The expected annual cycle can be seen in the timeseries; an increase in rSSM during the winter before decreasing over the spring and summer as the weather becomes drier, before increasing again during the autumn and winter. However, the soil moisture appears to increase over the summer, when anecdotally it can be expected to be at its lowest during this time of the year. This can be seen during the summer of 2018, when the rSSM values increase slightly over the course of the summer; a period of time when very little rainfall fell over the Thames region [2]. This apparent increase is not due to an increase in soil moisture, but due to an increase in radar backscatter, as the contribution from vegetation (predominately agricultural crops) increases over the growing season, before dropping away after the harvest. Current work is focussed on deriving a correction for seasonal variations in vegetation cover, based on multiple satellite viewing geometries.

Figure 2: (Top) rSSM images for the Kennet Catchment area. Areas denoted in grey are either outside the Kennet Catchment, or have been neglected as urban areas. (bottom) Spatially average rSSM values for the individual orbit (black line) and for a 7-orbit moving average (red line).

References

[1] Bauer-Marshallinger, B., Freeman, V., Cao, S., Paulic, C., Schaufler, S., Stachl, T., Modanesi, S., Massari, C., Brocca, L., and W. Wager, 2019: Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles, IEEE Trans. Geosci. Remote Sens., 57, 520-539, https://doi.org/10.1109/TGRS.2018.2858004

[2] Turner, S., Barker, L., Hannaford, J., Muchan, K., Parry, S., and C. Sefton, 2021: The 2018/2019 drought in the UK: a hydrological appraisal., Weather, 99, 1-6, https://doi.org/10.1002/wea.4003

 

 

Posted in Climate, earth observation, radar, Remote sensing, soil moisture | Leave a comment

Rescuing early satellite data to improve long-term estimates of past weather.

By: Jade Westfoot 

This post is contributed by Jade Westfoot, a year-12 school student who did work experience in the department recently. During her week with us, Jade worked with Drs. Jon Mittaz and Tom Hall on rescuing historic satellite data to make it more usable for historical weather analysis. Jade is passionate about science communication and is interested in both looking up at the sky and back down at Earth, and she is aiming to study a mixture of space science and Earth science!

Today, satellites are a fundamental part of our everyday lives, with a multitude of roles such as navigation, communication, space science and Earth observation. Earth observation is increasingly important in the race to understand our planet to combat and adapt to the climate crisis.

Nearly 1000 Earth observation satellites are available to help with this. Most orbit in sun synchronous or polar orbits, meaning that they fly over locations at a fixed time each day on an orbit that takes them pretty much over the poles. 1000 satellites seems like a lot, but many of them simply image the land beneath them, which is useful, but to understand the state of the atmosphere (in terms of temperature, humidity etc at different heights), more specific sensors are needed. For example, the European Centre for Medium-range Weather Forecasting (ECMWF) currently collects data from roughly 100 useful sensors to inform weather forecasts. However, we haven’t always had this wealth of information: as recently as the 1990s, ECMWF was using fewer than 15 satellites!

As well as forecasting, ECMWF (located in Reading) is an important centre for estimating the weather conditions of the past, which is immensely useful for environment and climate science, as well as engineering and planning. This ‘retrospective weather forecast’ is known in the field as “re-analysis”. The reduction in satellite information back in time is a challenge for re-analysis going back many decades. Mostly, satellite data have been introduced from the late 1970s onwards, but there are more measurements from earlier satellite missions that can be rescued and may be useful.

A good example is the Nimbus programme, NASA’s second programme of experimental Earth observation satellites, with 7 satellites successfully launched between 1964 and 1978. Over the lifetime of the programme the instruments changed, but during the 1960s one of the instruments for atmospheric sounding (used on Nimbus 3) was the MRIR sensor. MRIR was able to take measurements in 5 bands: 1 in the visible spectrum to detect reflected sunlight, and 4 in the infrared spectrum measuring radiation from Earth. Each infrared channel effectively measured different layers of the atmosphere by measuring the signal at different frequencies. For example, the 6.7μm channel was sensitive to radiation emitted by atmospheric water vapour, so by measuring it the MRIR data can be used to estimate the amount of water at a certain height in the atmosphere.

At the time, the Nimbus data was used to refine the accuracy of weather forecasting, and now it is hoped that accessing the data will help ECMWF improve re-analyses to understand long-term weather changes.

How do we know if the early data are valid?

Unfortunately, the age of the data brings with it some problems. Some of the data are missing, and the data that have survived are generally more noisy than modern instruments.

This doesn’t mean it’s useless though! We can infer things from each of the channels individually (such as the presence of clouds). To show their potential, we can combine the MRIR data into a false colour image, which can then be compared to photographs of the Earth. Where do we get photos of the Earth from space in the 1960s? Well, it just happens that some of these satellites were in operation at the same time as NASA’s Apollo missions, during which astronauts took many photos of the whole Earth.

Figure 1: Comparison between the Apollo photo and each of the MRIR channels

For example, looking at the figure you can see that Indonesia and Papua New Guinea are covered by clouds which share similar patterns between the photo and observation. This can be seen on both channel 1 (which measures water absorption) and channel 2, which tries to measure surface temperature but here is blocked by clouds.

The photo and MRIR data don’t perfectly match, which is expected: A photograph is an instantaneous capture of the whole Earth, taken from 400,000 km away, whereas the false colour image is generated from data taken by the satellites as they scan strips of the scene beneath them during their approximately 110 minute orbits. This means that the whole Earth is not captured at one time in the satellite view, so clouds can move and develop in the time it takes to build up the MRIR pictures. However, because of the distances, the MRIR measurements have a higher resolution (45 km) than the Apollo photo.

Comparing satellite data to the Apollo photo boosts our confidence in the data collected, as the similarity between the two independent observations generally confirms the MRIR data have been correctly ‘rescued’.

How will rescued data be used?

Simulations and re-analyses of the climate during the 1960s, including ECMWF’s, don’t take advantage of much old satellite data like that provided by Nimbus. Instead, they rely on in-situ data (measurements taken by ground stations or weather balloons). In situ data are highly informative, but are not available everywhere, particularly in the southern hemisphere. Satellites capture information about the whole planet.

Including the Nimbus data will mean future re-analyses can extend the timescale over which satellite data are used, to more than 50 years, making the re-analyses even more relevant for looking at weather changes over many decades. The more data from different sources we can put into a re-analysis, the more accurate it should become. Having accurate information about past weather will continue to be incredibly important in order to respond to the changing climate.

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The Future of Arctic Sea Ice

By: Rebecca Frew

It is well documented in scientific studies and the news (recent example here) that the summer extent of Arctic sea ice has been declining rapidly in response to global warming. As the summer sea ice shrinks and retreats Northward, the summer marginal ice zone (MIZ) has been widening and making up a larger proportion of the summer sea ice cover (Ralph et al. 2020).

The MIZ is typically defined as the area in which sea ice is influenced by waves. A more convenient definition often used in studies is the area where the sea ice concentration is between 15% and 80%. With the area above 80% defined as the ice pack where the sea ice floes are more densely packed together, blocking direct atmosphere-ocean interaction. The MIZ is typically small in the winter and grows to maximum extent in the summer as the ice pack fragments and melts, creating smaller and less densely packed floes.

Figure 1: Sea Ice floes. Image Credit:  Kevin Woods, NOAA Pacific Marine Environmental Laboratory. 

This trend of an increasingly MIZ dominated ice cover is projected to continue (Strong & Rigor 2013, Aksenov et al. 2017), transitioning to sea ice free Arctic summers. The relative rates and importance of sea ice processes in the MIZ differs to those in the ice pack. This has consequences for the exchange of heat and salt between the atmosphere and ocean, and ultimately the date at which the Arctic becomes ice free in summer.

Three processes that differ between the MIZ and ice pack are the lateral melt rate (melting on the side of the floes), basal/bottom melting of the floes and breakup of floes caused by waves. The average floe size in the MIZ is smaller than in the ice pack, which means the increases the perimeter to area ratio and promotes faster lateral melting. Ice thickness also tends to be thinner, which increases the rate of basal ice melting in the summer. Smaller, less densely packed sea ice floes in the MIZ are more susceptible to wave breakup, creating smaller floes which tend to melt at a quicker rate.

Figure 2: Arctic sea ice and MIZ extent in the 1980s and the 2010s, from a sea ice model simulation.

In my research, I am investigating the relative importance of growth and melt processes in the MIZ and whether they might change in the future. As part of this, I am considering how they are currently represented in climate models, whether this is accurate and how sensitive the processes are to parameters that are difficult to constrain from observations. For example, a relatively recent area of development in sea ice models is the inclusion of a floe size distribution (Roach et al. 2018). Previously sea ice floes were all one size or ignored in models, now a distribution floe sizes across a range of sizes is calculated within each grid cell, better representing the variation of cm to 100s of kms that is observed. This is important when modelling the MIZ because floe sizes are smaller, and the floe size influences the lateral melt rate.

How lateral melt rate differs in the MIZ from the ice pack, and how it might change in the future are a couple of the questions I am trying to answer. Answering these questions about processes in the MIZ helps to improve projections of Arctic sea ice, and better represent the response of Arctic sea ice to different future scenarios of warming.

References

Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. J. G., Williams, T. D., Bertino, L., and Bergh, J., 2017: On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice, Mar. Pol., 75, 300–317, https://doi.org/10.1016/j.marpol.2015.12.027,

Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M., 2018: An Emergent Sea Ice Floe Size Distribution in a Global Coupled Ocean-Sea Ice Model, J. Geophys. Res.-Ocean, 123, 4322–4337, https://doi.org/10.1029/2017JC013692

Rolph, R. J., Feltham, D. L., and Schröder. D., 2020: Changes of the Arctic marginal ice zone during the satellite era, The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020

Strong, C., and Rigor, I. G., 2013: Arctic marginal ice zone trending wider in summer and narrower in winter, Geophys. Res. Lett., 40, 4864–4868, https://doi.org/10.1002/grl.50928

Posted in Arctic, Climate, Climate change, Cryosphere, Polar | Leave a comment

Three Flavours of Pykrete

By: David Livings

Three Flavours of Pykrete

A few years ago, Giles Foden published a novel called Turbulence. Most of the book is about a young meteorologist in the second world war, but there’s a framing story set in the 1980s, in which the same man is sailing from Antarctica to Saudi Arabia in a ship made from a mixture of ice and frozen wood pulp called Pykerete. Pykerete was named after Geoffrey Pyke, who proposed building giant aircraft carriers from such a material. Some of the characters in the book are real people, some are fictionalised versions of real people, and some are completely made up. Pyke and Pykerete were obviously made up …

Or so I thought. I subsequently learnt that Geoffrey Nathaniel Joseph Pyke (1893–1948) really did exist or is else a very elaborate hoax, of which the Oxford Dictionary of National Biography is either a victim or a perpetrator. Not only did Pyke propose building aircraft carriers from ice, but he got taken seriously (at least for a while). Pykrete (sometimes spelt Pykerete or Pykecrete) was named after him, but was not actually his invention. The initial idea of adding wood pulp to ice to increase its strength came from two researchers at the Brooklyn Polytechnic, and its properties were investigated at Pyke’s request by the chemist Max Perutz, who would go on to win the Nobel Prize for Chemistry for his work on the structure of haemoglobin. Perutz published a paper on pykrete in the Journal of Glaciology in 1948.

Last year, in a change of career direction, I moved from meteorological research to software engineering on a sea ice model. As part of my familiarisation with the new field, I thought it would be a good idea to carry out some experiments on the substances being modelled. The first experiment was to investigate the difference between fresh water ice and salt water ice. I made samples of both in plastic pots that originally contained desserts from a supermarket (dimensions: 45 mm diameter at bottom, 70 mm at top, height 88 mm, but only filled to 66 mm for the experiment). The salt water ice contained enough table salt to cover the bottom of the pot to a depth of 1–2 mm before adding the water. Both samples were frozen in a domestic freezer for over 24 h, and then taken out and attacked from the top with a blunt-ended table knife. The knife didn’t penetrate the fresh ice, but just sent up some ice chips. It did penetrate the salt ice, which had a mushier texture.

It was at this point that I remembered Pyke and pykrete, and decided to make some for myself. A good place to start an investigation of pykrete is the web page of Peter Goodeve, which takes a critical look at some of the myths that have grown up about the substance. It also contains links to other sources (some of which perpetuate the myths).

Sources differ over whether the magic ingredient in pykrete is wood pulp, wood powder, sawdust, or wood chips. I had none of these available, but I did have a bag of what described itself as Oatbran & Wheatbran Porridge Oats, so I improvised with that. In one of the pots I mixed dry porridge with just enough water to cover it. I filled the other pot with plain water to the same depth, which was about 30 mm. After freezing both samples, I turned them out of their pots and hit them with a hammer. The plain ice shattered after one blow. The porridge ice survived three blows, only denting. This substance was definitely tougher than plain ice.

This experiment with frozen porridge left a couple of things to be desired. Firstly, the additive wasn’t one of the classic pykrete additives. Secondly, the way in which the amount of additive was determined was rather crude. Perutz reports good results with 4–14% wood pulp.

Recently I was able to obtain some fine sawdust, and decided to repeat the experiment using this and other additives. As well as sawdust and porridge, I followed Goodeve’s suggestion of reverse engineering wood pulp by using torn up newspaper. Rather than tearing up the newspaper (actually three pages from the LRB) I cut it into tiny pieces a few millimetres across. If doing this yourself, allow at least two hours.

I used 20 g of each additive to 200 ml of water. One quarter of the mixture was used to make small samples as in the previous experiment, and the rest was used to make larger samples in another type of dessert pot (sample dimensions: 60 mm diameter at bottom, 77 mm at top, height 40 mm). On making the mixtures, it became clear that the additive settling to the bottom was going to be a problem and also that the experiment last year had used much more than 10% porridge. To guard against settling, I took the mixtures out of the freezer and stirred them every half hour for the first three and a half hours. The following figures show the large samples before and after being hit with a hammer.

Figure 1. Samples of plain ice and the three flavours of pykrete beside their additives. Top left: plain ice. Top right: sawdust. Bottom left: porridge. Bottom right: newspaper.

Figure 2. The results of hitting the samples with a hammer. Top left: the plain ice split after two blows. Top right: the sawdust pykrete survived six blows with little damage. Bottom left: the porridge pykrete split after five blows. Bottom right: the newspaper pykrete survived six blows.

Results from the small samples were similar. The plain ice shattered after one blow, sending fragments flying across the room. The porridge pykrete split after two blows. The sawdust and newspaper pykretes survived three blows.

Conclusion: Sawdust pykrete and newspaper pykrete are tougher than plain ice. Porridge pykrete at the same density is intermediate in strength, but at higher densities is impressive.

Acknowledgements

The author thanks Debbie Turner and Ian Shankland for providing the sawdust.

References

Perutz, M. F., 1948: A description of the iceberg aircraft carrier and the bearing of the mechanical properties of frozen wood pulp upon some problems of glacier flow. J. Glaciol.1, 95–104, https://doi.org/10.3189/S0022143000007796.

Posted in Climate, Cryosphere, History of Science | Leave a comment

Can You Guess The Ingredients Of A Cake?

By: Amos Lawless

“Mmm this cake is lovely, what’s in it?” “Try to guess!” How often have we had that response from a friend or colleague who is proud of the cake they have just baked? And we usually try to guess the main ingredients – “I think there must be ginger or cinnamon. And can I taste lemon?”. But what if that friend persisted and asked you to try to guess all the ingredients – how many eggs they have used, how many grams of sugar are in the cake and how much butter it contains? Maybe you’d think they’d gone a bit crazy! Surely it is impossible to work out all the ingredients just by tasting it? It may sound unreasonable, but this is effectively what we try to do each day to interpret satellite measurements for our weather forecasts.

Weather satellites, besides giving us the nice pictures that we see on television, provide a wealth of other information about the atmosphere. Satellites actually measure the radiation emitted from the atmosphere at different frequencies, and these measurements depend on the properties of that part of the atmosphere that the satellite is looking at, such as its temperature, humidity and winds. It is as if these “ingredients” of the atmosphere are brought together into a “cake” that the satellite can taste. But what we are really interested in knowing is these ingredients. So how can we split the satellite measurement back into its atmospheric ingredients?

Thankfully we have a mathematical technique for doing this, which we call data assimilation. Each satellite instrument can measure at many different frequencies (as if they have many “taste buds” sensitive to different ingredients), so by combining measurements from different satellites in an intelligent way, as well as other more conventional measurements made on the ground, data assimilation helps us to build up a complete picture of the atmosphere all around the globe. This is done every day as part of modern weather forecasting, since knowing what the atmosphere is like now is essential if we are to make accurate forecasts. Most data assimilation techniques are based on finding an optimal combination of what we think is the current state of the atmosphere and our measurements, taking into account the precision of the different pieces of information we have. Writing down the theory of how to do this is fairly easy, but putting into practice is usually much harder.

Scientists of the Data Assimilation Research Centre (DARC) at the University of Reading work on a variety of problems related to data assimilation, from developing new approaches to applying it in practice. Each year, jointly with the National Centre for Earth Observation (NCEO), we organise a training course for scientists round the world to learn about the theory of data assimilation and how to apply it in practice. Lectures from DARC scientists are combined with computer practical exercises, so that participants can learn the theory of data assimilation and get a feel for how different methods perform in practice. Normally the course is held in-person, but this year there was the challenge of whether it was possible to hold it online. So it was that at the start of May our first ever training course on data assimilation using Microsoft Teams took place. Joining were 29 scientists from the UK, Belgium, Bulgaria, Denmark, Germany, Greece, Italy, Spain and the USA, working in universities, research institutes and meteorological forecasting centres.

Figure 1: Lecture by DARC scientist Dr Javier Amezcua

So how did we do it? By now we are already used to giving and listening to talks online, so the lecture part of the course was fairly straightforward. However, an important aspect of a course such as this is that it is interactive, with the possibility to ask questions. Thankfully the chat function worked well here, with participants putting questions in the chat continually and other DARC scientists responding if it wasn’t necessary to interrupt the lecture. Then computer practical exercises took place in breakout rooms, with groups of three participants working together. And during the breaks informal discussions took place using Gather.Town (a very impressive tool that I have only just discovered), including use of a virtual whiteboard to discuss further the mathematics. So what did the participants say about the online delivery? Comments included “I think the format worked really well”, “the arrangements for the remote delivery of the course were excellent”, “I think the practicals were organised well with lecturers rotating and coming to different rooms. That made me feel like I was in a classroom with having constant access to help”. Running this course certainly taught us a lot about how to teach data assimilation online, with lots of lessons learnt for the future. But everybody also realised that there are limitations to such a format. Hopefully next year we will be able to run the course in person again, with the opportunity for more informal discussions over coffee … and plenty of cake!

Figure 2: Online group photo of some of the lecturers and participants.

References

Data Assimilation Research Centre (n.d.), What is data assimilation? https://research.reading.ac.uk/met-darc/aboutus/what-is-data-assimilation/

Data Assimilation Research Centre (2019). Online lecture notes from 2019 training course.
https://research.reading.ac.uk/met-darc/training/ecmwf2019/

Lawless, A.S. (2013), Variational data assimilation for very large environmental problems. In Large Scale Inverse Problems: Computational Methods and Applications in the Earth Sciences (2013), Eds. Cullen, M.J.P., Freitag, M. A., Kindermann, S., Scheichl, R., Radon Series on Computational and Applied Mathematics 13. De Gruyter, pp. 55-90.

Nichols, N.K. (2009), Mathematical concepts of data assimilation. Preprint MPS_2009-04. Department of Mathematics, University of Reading.
http://www.reading.ac.uk/nmsruntime/saveasdialog.aspx?lID=48408&sID=90309

 

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